Useful R functions

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Sandbox vectors

Let’s define some vectors which can be used for demonstrations:

manyNumbers <- sample( 1:1000, 20 )
manyNumbers

 [1]  54  49 578 113 919 489 915 367 344 180  63 270 900 281 553 320 778 358 717 331

manyNumbersWithNA <- sample( c( NA, NA, NA, manyNumbers ) )
manyNumbersWithNA

 [1] 331 180  49 915 578 919 900 717 113 358  NA 344 367 778 553 320  63 489 270  NA  NA  54 281

duplicatedNumbers <- sample( 1:5, 10, replace = TRUE )
duplicatedNumbers

 [1] 2 3 1 3 1 4 3 2 5 4

letters

 [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t" "u" "v" "w" "x" "y" "z"

LETTERS

 [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S" "T" "U" "V" "W" "X" "Y" "Z"

mixedLetters <- c( sample( letters, 5 ), sample( LETTERS, 5 ) )
mixedLetters

 [1] "t" "e" "j" "p" "v" "L" "C" "U" "X" "D"

Are all/any elements TRUE

• Input: logical vector
• Output: single logical value
• Task: try, understand what happens when you use manyNumbersWithNA instead of manyNumbers.

all( manyNumbers <= 1000 )

[1] TRUE

all( manyNumbers <= 500 )

[1] FALSE

any( manyNumbers > 1000 )

[1] FALSE

any( manyNumbers > 500 )

[1] TRUE

all( !is.na( manyNumbers ) )

[1] TRUE

any( is.na( manyNumbers ) )

[1] FALSE

Which elements are TRUE

Input: logical vector Output: vector of numbers (positions)

which( manyNumbers > 900 )

[1] 5 7

which( manyNumbersWithNA > 900 )

[1] 4 6

which( is.na( manyNumbersWithNA ) )

[1] 11 20 21

Filtering vector elements

• Input: any vector and filtering condition
• Output: elements of the input vector
• Note: several ways to get the same effect

manyNumbers[ manyNumbers > 900 ] # indexing by logical vector

[1] 919 915

manyNumbers[ which( manyNumbers > 900 ) ] # indexing by positions

[1] 919 915

somePositions <- which( manyNumbers > 900 )
manyNumbers[ somePositions ]

[1] 919 915

Are some elements among other elements

• Input: two vectors
• Output: a logical vector corresponding to the first input vector

"A" %in% LETTERS

[1] TRUE

c( "X", "Y", "Z" ) %in% LETTERS

[1] TRUE TRUE TRUE

all( c( "X", "Y", "Z" ) %in% LETTERS )

[1] TRUE

all( mixedLetters %in% LETTERS )

[1] FALSE

any( mixedLetters %in% LETTERS )

[1] TRUE

mixedLetters[ mixedLetters %in% LETTERS ]

[1] "L" "C" "U" "X" "D"

mixedLetters[ !( mixedLetters %in% LETTERS ) ]

[1] "t" "e" "j" "p" "v"

manyNumbers %in% 300:600

 [1] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
[18]  TRUE FALSE  TRUE

which( manyNumbers %in% 300:600 )

[1]  3  6  8  9 15 16 18 20

sum( manyNumbers %in% 300:600 )

[1] 8

Pick one of two (three) depending on condition

• Input: a logical vector and two vectors additional vectors (for TRUE, for FALSE)
• Output: elements of the additional vectors
• Note: it can take care of NAs

if_else( manyNumbersWithNA >= 500, "large", "small" )

 [1] "small" "small" "small" "large" "large" "large" "large" "large" "small" "small" NA      "small" "small"
[14] "large" "large" "small" "small" "small" "small" NA      NA      "small" "small"

if_else( manyNumbersWithNA >= 500, "large", "small", "UNKNOWN" )

 [1] "small"   "small"   "small"   "large"   "large"   "large"   "large"   "large"   "small"   "small"  
[11] "UNKNOWN" "small"   "small"   "large"   "large"   "small"   "small"   "small"   "small"   "UNKNOWN"
[21] "UNKNOWN" "small"   "small"  

# here integer 0L is needed instead of real 0.0 
# manyNumbersWithNA contains integer numbers and the method complains
if_else( manyNumbersWithNA >= 500, manyNumbersWithNA, 0L ) 

 [1]   0   0   0 915 578 919 900 717   0   0  NA   0   0 778 553   0   0   0   0  NA  NA   0   0

Duplicates and unique elements

• Input: a vector

unique( duplicatedNumbers )

[1] 2 3 1 4 5

unique( c( NA, duplicatedNumbers, NA ) )

[1] NA  2  3  1  4  5

duplicated( duplicatedNumbers )

 [1] FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE

Positions of max/min elements

which.max( manyNumbersWithNA )

[1] 6

manyNumbersWithNA[ which.max( manyNumbersWithNA ) ]

[1] 919

which.min( manyNumbersWithNA )

[1] 3

manyNumbersWithNA[ which.min( manyNumbersWithNA ) ]

[1] 49

range( manyNumbersWithNA, na.rm = TRUE )

[1]  49 919

Sorting/ordering of vectors

manyNumbersWithNA

 [1] 331 180  49 915 578 919 900 717 113 358  NA 344 367 778 553 320  63 489 270  NA  NA  54 281

sort( manyNumbersWithNA )

 [1]  49  54  63 113 180 270 281 320 331 344 358 367 489 553 578 717 778 900 915 919

sort( manyNumbersWithNA, na.last = TRUE )

 [1]  49  54  63 113 180 270 281 320 331 344 358 367 489 553 578 717 778 900 915 919  NA  NA  NA

sort( manyNumbersWithNA, na.last = TRUE, decreasing = TRUE )

 [1] 919 915 900 778 717 578 553 489 367 358 344 331 320 281 270 180 113  63  54  49  NA  NA  NA

manyNumbersWithNA[1:5]

[1] 331 180  49 915 578

order( manyNumbersWithNA[1:5] )

[1] 3 2 1 5 4

rank( manyNumbersWithNA[1:5] )

[1] 3 2 1 5 4

sort( mixedLetters )

 [1] "C" "D" "e" "j" "L" "p" "t" "U" "v" "X"

Ranking of vectors

manyDuplicates <- sample( 10:15, 10, replace = TRUE )
rank( manyDuplicates )

 [1] 1.0 7.0 3.5 3.5 7.0 3.5 9.5 7.0 3.5 9.5

rank( manyDuplicates, ties.method = "min" )

 [1] 1 6 2 2 6 2 9 6 2 9

rank( manyDuplicates, ties.method = "random" )

 [1]  1  7  5  4  6  2 10  8  3  9

Rounding numbers

v <- c( -1, -0.5, 0, 0.5, 1, rnorm( 10 ) )
v

 [1] -1.00000000 -0.50000000  0.00000000  0.50000000  1.00000000 -0.67396428 -0.13518803 -0.76440720
 [9] -0.96543267 -0.36329583 -0.98591783  0.44122914  0.06678965 -0.15158257 -0.84128778

round( v, 0 )

 [1] -1  0  0  0  1 -1  0 -1 -1  0 -1  0  0  0 -1

round( v, 1 )

 [1] -1.0 -0.5  0.0  0.5  1.0 -0.7 -0.1 -0.8 -1.0 -0.4 -1.0  0.4  0.1 -0.2 -0.8

round( v, 2 )

 [1] -1.00 -0.50  0.00  0.50  1.00 -0.67 -0.14 -0.76 -0.97 -0.36 -0.99  0.44  0.07 -0.15 -0.84

floor( v )

 [1] -1 -1  0  0  1 -1 -1 -1 -1 -1 -1  0  0 -1 -1

ceiling( v )

 [1] -1  0  0  1  1  0  0  0  0  0  0  1  1  0  0

Naming vector elements

heights <- c( Amy = 166, Eve = 170, Bob = 177 )
heights

Amy Eve Bob 
166 170 177 

names( heights )

[1] "Amy" "Eve" "Bob"

names( heights ) <- c( "AMY", "EVE", "BOB" )
heights

AMY EVE BOB 
166 170 177 

heights[[ "EVE" ]]

[1] 170

Generating grids

expand_grid( x = c( 1:3, NA ), y = c( "a", "b" ) )

# A tibble: 8 x 2
      x y    
  <int> <chr>
1     1 a    
2     1 b    
3     2 a    
4     2 b    
5     3 a    
6     3 b    
7    NA a    
8    NA b    

Generating combinations

combn( c( "a", "b", "c", "d", "e" ), m = 2, simplify = TRUE )

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a"  "a"  "a"  "a"  "b"  "b"  "b"  "c"  "c"  "d"  
[2,] "b"  "c"  "d"  "e"  "c"  "d"  "e"  "d"  "e"  "e"  

combn( c( "a", "b", "c", "d", "e" ), m = 3, simplify = TRUE )

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a"  "a"  "a"  "a"  "a"  "a"  "b"  "b"  "b"  "c"  
[2,] "b"  "b"  "b"  "c"  "c"  "d"  "c"  "c"  "d"  "d"  
[3,] "c"  "d"  "e"  "d"  "e"  "e"  "d"  "e"  "e"  "e"  

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